Execution replay and debugging

As most parallel and distributed programs are internally non-deterministic -- consecutive runs with the same input might result in a different program flow -- vanilla cyclic debugging techniques as such are useless. In order to use cyclic debugging tools, we need a tool that records information about an execution so that it can be replayed for debugging. Because recording information interferes with the execution, we must limit the amount of information and keep the processing of the information fast. This paper contains a survey of existing execution replay techniques and tools.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AADebug 2000), August 2000, Munich. cs.SE/001003

Learning from the Success of MPI

The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers. This success has occurred in spite of the view of many that message passing is difficult and that other approaches, including automatic parallelization and directive-based parallelism, are easier to use. This paper argues that MPI has succeeded because it addresses all of the important issues in providing a parallel programming model.Comment: 12 pages, 1 figur

POSH: Paris OpenSHMEM: A High-Performance OpenSHMEM Implementation for Shared Memory Systems

In this paper we present the design and implementation of POSH, an Open-Source implementation of the OpenSHMEM standard. We present a model for its communications, and prove some properties on the memory model defined in the OpenSHMEM specification. We present some performance measurements of the communication library featured by POSH and compare them with an existing one-sided communication library. POSH can be downloaded from \url{http://www.lipn.fr/~coti/POSH}. % 9 - 67Comment: This is an extended version (featuring the full proofs) of a paper accepted at ICCS'1

Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI

The computational difficulty of econometric problems has increased dramatically in recent years as econometricians examine more complicated models and utilize more sophisticated estimation techniques. Many problems in econometrics are `embarrassingly parallel' and can take advantage of parallel computing to reduce the wall clock time it takes to solve a problem. In this paper I demonstrate a method that can be used to solve a maximum likelihood problem using the MPI message passing library. The econometric problem is a simple multinomial logit model that does not require parallel computing but illustrates many of the problems one would confront when estimating more complicated models

State-of-the-Art in Parallel Computing with R

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly useful for general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems four different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix

State of the Art in Parallel Computing with R

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.